title: Lymph node involvement in immunohistochemistry-based molecular classifications of breast cancer.
abstract: BACKGROUND
  Prognosis and treatment options differ for each molecular subtype of breast cancer, but risk of regional lymph node (LN) metastasis for each subtype has not been well studied. Since LN status is the most important predictor for prognosis, the aim of this study is to investigate the propensity for LN metastasis in each of the five breast cancer molecular subtypes.
  
  
  METHODS
  Under an institutional review board-approved protocol, we retrospectively reviewed the charts of all pathologically confirmed breast cancer cases from January 2004 to June 2012. Five subtypes were defined as luminal A (hormone receptor positive, Ki-67 low), luminal B (hormone receptor positive, Ki-67 high), luminal human epidermal growth factor receptor 2 (HER2), HER2-enriched (hormone receptor negative), and triple negative (TN).
  
  
  RESULTS
  A total of 375 patients with complete data were classified by subtype: 95 (25.3%) luminal A, 120 (32%) luminal B, 69 (18.4%) luminal HER2, 26 (6.9%) HER2-enriched, and 65 (17.3%) TN. On univariate analysis, age (<50), higher tumor grade, HER2+ status, tumor size, and molecular subtype were significant for LN positivity. Molecular subtype correlated strongly with tumor size (χ(2); P = 0.0004); therefore, multivariable logistic regression did not identify molecular subtype as an independent variable to predict LN positivity.
  
  
  CONCLUSIONS
  Luminal A tumors have the lowest risk of LN metastasis, whereas luminal HER2 subtype has the highest risk of LN metastasis. Immunohistochemical-based molecular classification can be readily performed and knowledge of the factors that affect LN status may help with treatment decisions.
authors.affiliation: Department of Surgery, University of Texas Medical Branch, Galveston, Texas.
authors.forename: Nicholas K
authors.initials: NK
authors.lastname: Howland
authors.forename: Teryn D
authors.initials: TD
authors.lastname: Driver
authors.forename: Michael P
authors.initials: MP
authors.lastname: Sedrak
authors.forename: Xianfeng
authors.initials: X
authors.lastname: Wen
authors.forename: Wenli
authors.initials: W
authors.lastname: Dong
authors.forename: Sandra
authors.initials: S
authors.lastname: Hatch
authors.forename: Mahmoud A
authors.initials: MA
authors.lastname: Eltorky
authors.forename: Celia
authors.initials: C
authors.lastname: Chao
mesh_terms: D014408:Biomarkers, Tumor / Q000378:metabolism*; D001943:Breast Neoplasms / Q000145:classification / Q000453:epidemiology / Q000556:secondary*; D066246:ErbB Receptors / Q000378:metabolism; D005260:Female; D006801:Humans; D007150:Immunohistochemistry / Q000379:methods*; D019394:Ki-67 Antigen / Q000378:metabolism; D016015:Logistic Models; D008207:Lymphatic Metastasis / Q000473:pathology; D008875:Middle Aged; D015999:Multivariate Analysis; D011237:Predictive Value of Tests; D011379:Prognosis; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000378:metabolism; D012189:Retrospective Studies; D012307:Risk Factors; D064726:Triple Negative Breast Neoplasms / Q000145:classification / Q000453:epidemiology / Q000556:secondary*
chemical_list: D014408:Biomarkers, Tumor; D066246:ErbB Receptors; D019394:Ki-67 Antigen; D018719:Erb-b2 Receptor Tyrosine Kinases; C512478:EGFR protein, human; C508053:ERBB2 protein, human
keywords: Immunohistochemistry; Molecular subtypes of breast cancer